【作者】 卢瑞章；

【导师】 李智录；

【作者基本信息】 西安理工大学 ， 农业水土工程， 2005， 硕士

【摘要】 大坝的安全状态不仅直接影响着工程经济效益的发挥,而且关系到下游居民的生命财产安全乃至整个社会的稳定。因此,开发和完善一套实用可靠的大坝安全监测系统对企业、社会以及广大人民群众都具有极其重大的意义。 本文结合黑河、石头河、石砭峪、涧峪等水库大坝的监测项目,在分析和总结各大坝共性和个性的基础上,以统计模型作为对比,研究了人工神经网络模型及其在大坝安全分析中的应用,并开发出大坝监测分析系统,大大增加了大坝状况分析的速度和便利性。 主要研究内容和成果如下: (1) 在对目前常用数据预处理算法的研究分析基础上,建立了一种能够实时进行粗差(野值)识别、数据整编的预处理算法,实例测试表明其具有很好的适应性,处理效果较为理想。 (2) 简单讨论了大坝统计模型的因子选择依据,分析了统计模型建模的局限性。 (3) 测试了RBF神经网络特有的局部性能,并在理论上对这些特性做了分析说明;针对其特点设计出一种自动增加模式节点的在线和快速离线学习算法,实例测试表明该算法在速度和在线记忆等性能上较其他都有很大的提高。 (4) 在不同条件下对RBF网络与统计回归模型做了测试对比,结果表明RBF神经网络建模的条件比较宽松,对输入因子相关性的敏感度很小,建模简单方便。 (5) 采用先进的开发平台和工具,开发出了基于B/S结构的大坝安全监测数据西安理工大学硕士学位论文分析系统。该系统功能完备,界面友好,可操作性强,自动化程度高。另外,高度的模块化结构使系统具有良好的重用和扩充特性,便于在不同_L_程中使用。关键词:大坝安全监测,人工神经网络,RBF,B/S结构更多还原

【Abstract】 The security state of dam not only influences the exertion of economic benefits of projects directly, but concerns the safety of life and property of downstream residents and even the stability of the whole society. Therefore, it is very significant to exploit a practical and credible dam safety monitoring system for enterprise, society and the broad masses of the people.Combined with the monitoring projects of the Black River, the Stone River, the Shibianyu River and the Jianyu River, based on analyzing and summarizing the commonness and individuality of these dams, compared with statistic model, researches were carried out on Artificial Neural Networks model and its use in the dam safety analysis. Besides, a dam safety monitoring system was developed, which increases the speed and facility of dam safety analysis greatly.Major contents and findings are as follows:(1) Based on the analysis of monitored data of dam and common arithmetic, a data pretreatment arithmetic was established which could identify and reorganize the data real-timely. The testes indicate that it has great adaptability and perfect treatment effect.(2) The basis of the factor chooses for statistical model of dam monitoring was simply analyzed, as well as its limitation of building model.(3) The part performances of RBF Neural Networks were tested and analyzed theoretically. Based on the characteristics of RBFNN, a celerity off-line and on-line training arithmetic which could add nodes automatically was designed and the test indicates that this arithmetic enhances the velocity and study ability greatly compared with others.(4) The tests on RBF and statistical model in different conditions indicate that the former is insensitive to the factor correlativity of input-data and used simply and conveniently.(5) By using the advanced developing platform and tools, the dam safety monitoring system was developed based on B/S frame. The system has the characters of perfect function, friendly interface, good effectiveness and high automatic degree. In addition, the powerful module structure makes the system have a good reuse and expansion characteristic.更多还原